Post date: Sep 14, 2015 11:1:16 PM
All files and scripts mentioned here are in /labs/evolution/projects/cmaclentil/popgen/abc/ and its sub-directories, and raw ABC simulations are in sims.
Two scripts were used to iterate over SNPs to estimate model probabilities (constrained = parallelism vs unconstrained) and selection coefficients: summarizeLentil.pl and summarizeReversion.pl. These scripts call the ABC package in R and make inferences using either multinomial logistic or local-linear regression corrections with a tolerance of 1e-3 (2000 simulations out of 2 million). These produced the files pp* and sel* which are subset by lentil vs. reversion (L vs R = mung) and constant selection vs. rapid adaptaiton. The pp* files contain model posterior probabilities (probability of the unconstrained model) whereas the sel* files include posterior medians, 25th, 75th, 2.5th and 97.5th quantiles for each line.
Next I summarized the selection estimates and evidence of parallelism with the following scripts (each pasted below with its content). The main take-home points were: there is some parallelism, particularly with rapid adaptation, but there is more evidence of idiosyncratic selection (or at least response to selection), and there is clear evidence of antagonistic pleiotropy, i.e. it looks like tradeoffs are real in this system.
Plots to examine parallelism (parallel.R):
library(RColorBrewer)
pp<-read.table("pp_lentil.txt",header=FALSE)
ppr<-read.table("pprapid_lentil.txt",header=FALSE)
L<-read.table("sel_lentil.txt",header=FALSE)
Lr<-read.table("selrapid_lentil.txt",header=FALSE)
cl<-1.7
ca<-1.2
cm<-1.6
pdf("parallelism.pdf",width=5,height=12)
par(mfrow=c(3,1))
par(mar=c(4.5,5.5,3,0.5))
myc<-brewer.pal(3,"Set1")
plot(sort(1-pp[,1]),cex.lab=cl,cex.axis=ca,type='l',xlab="locus (SNP)",ylab="posterior probability")
lines(sort(1-ppr[,1]),col="blue")
legend(0,0.99,c("constant selection","rapid adaptation"),lty=1,col=c("black","blue"))
title(main="(a) probability of parallel selection",adj=0,cex.main=cm)
plot(1-pp[,1],L[,1],ylim=c(-0.24,0.22),cex.lab=cl,cex.axis=ca,cex=0.7,col=myc[1],xlab="probability of parallelism",ylab="selection coefficient")
points(1-pp[,1],L[,6],cex=0.7,pch=22,col=myc[2])
points(1-pp[,1],L[,11],cex=0.7,pch=24,col=myc[3])
title(main="(b) parallelism vs. S for constant selection",adj=0,cex.main=cm)
plot(1-ppr[,1],Lr[,1],ylim=c(-0.64,0.48),cex.lab=cl,cex.axis=ca,cex=0.7,col=myc[1],xlab="probability of parallelism",ylab="selection coefficient")
points(1-ppr[,1],Lr[,6],cex=0.7,pch=22,col=myc[2])
points(1-ppr[,1],Lr[,11],cex=0.7,pch=24,col=myc[3])
title(main="(c) parallelism vs. S for rapid adaptation",adj=0,cex.main=cm)
legend(0,0.48,c("L1","L2","L3"),pch=c(21,22,24),col=myc)
dev.off()
Analyses and plots related to selection in lentil ignoring the reversion lines (lentil.R):
library(RColorBrewer)
L<-read.table("sel_lentil.txt",header=FALSE)
## s plots
pdf("sL.pdf",width=12,height=12)
par(mfrow=c(3,1))
par(mar=c(4.7,5.5,3,0.5))
sym<-19
plot(1:673,L[,1],ylim=c(-0.45,0.45),cex.lab=2,cex.axis=1.4,xlab="",ylab="selection on lentil",pch=sym,cex=0.6,type="n")
segments(1:673,L[,4],1:673,L[,5],lwd=0.35,lty=3,col="darkgray")
points(1:673,L[,1],pch=sym,cex=0.25)
abline(h=0,lty=2,lwd=1.1)
title(main="(a) L1",adj=0,cex.main=2)
plot(1:673,L[,6],ylim=c(-0.45,0.45),cex.lab=2,cex.axis=1.4,xlab="",ylab="selection on lentil",pch=sym,cex=0.6,type="n")
segments(1:673,L[,9],1:673,L[,10],lwd=0.35,lty=3,col="darkgray")
points(1:673,L[,6],pch=sym,cex=0.25)
abline(h=0,lty=2,lwd=1.1)
title(main="(b) L2",adj=0,cex.main=2)
plot(1:673,L[,11],ylim=c(-0.45,0.45),cex.lab=2,cex.axis=1.4,xlab="locus (SNP)",ylab="selection on lentil",pch=sym,cex=0.6,type="n")
segments(1:673,L[,14],1:673,L[,15],lwd=0.35,lty=3,col="darkgray")
points(1:673,L[,11],pch=sym,cex=0.25)
abline(h=0,lty=2,lwd=1.1)
title(main="(c) L3",adj=0,cex.main=2)
dev.off()
## number of loci with non-zero s
sum(L[,4] > 0 | L[,5] < 0)
sum(L[,9] > 0 | L[,10] < 0)
sum(L[,14] > 0 | L[,15] < 0)
ids<-read.table("locusids.txt",header=FALSE)
length(unique(ids[(L[,4] > 0 | L[,5] < 0),1]))
length(unique(ids[(L[,9] > 0 | L[,10] < 0),1]))
length(unique(ids[(L[,14] > 0 | L[,15] < 0),1]))
Lx<-L
## rapid adaptation
L<-read.table("selrapid_lentil.txt",header=FALSE)
## s plots
pdf("sLrapid.pdf",width=12,height=12)
par(mfrow=c(3,1))
par(mar=c(4.7,5.5,3,0.5))
sym<-19
plot(1:673,L[,1],ylim=c(-0.8,0.8),cex.lab=2,cex.axis=1.4,xlab="",ylab="selection on lentil",pch=sym,cex=0.6,type="n")
segments(1:673,L[,4],1:673,L[,5],lwd=0.35,lty=3,col="darkgray")
points(1:673,L[,1],pch=sym,cex=0.25)
abline(h=0,lty=2,lwd=1.1)
title(main="(a) L1",adj=0,cex.main=2)
plot(1:673,L[,6],ylim=c(-0.8,0.8),cex.lab=2,cex.axis=1.4,xlab="",ylab="selection on lentil",pch=sym,cex=0.6,type="n")
segments(1:673,L[,9],1:673,L[,10],lwd=0.35,lty=3,col="darkgray")
points(1:673,L[,6],pch=sym,cex=0.25)
abline(h=0,lty=2,lwd=1.1)
title(main="(b) L2",adj=0,cex.main=2)
plot(1:673,L[,11],ylim=c(-0.8,0.8),cex.lab=2,cex.axis=1.4,xlab="locus (SNP)",ylab="selection on lentil",pch=sym,cex=0.6,type="n")
segments(1:673,L[,14],1:673,L[,15],lwd=0.35,lty=3,col="darkgray")
points(1:673,L[,11],pch=sym,cex=0.25)
abline(h=0,lty=2,lwd=1.1)
title(main="(c) L3",adj=0,cex.main=2)
dev.off()
## number of loci with non-zero s
sum(L[,4] > 0 | L[,5] < 0)
sum(L[,9] > 0 | L[,10] < 0)
sum(L[,14] > 0 | L[,15] < 0)
ids<-read.table("locusids.txt",header=FALSE)
length(unique(ids[(L[,4] > 0 | L[,5] < 0),1]))
length(unique(ids[(L[,9] > 0 | L[,10] < 0),1]))
length(unique(ids[(L[,14] > 0 | L[,15] < 0),1]))
Plots and analyses of tradeoffs in the constant selection model (tradeoffs.R):
library(RColorBrewer)
L<-read.table("sel_reversionL.txt",header=FALSE)
R<-read.table("sel_reversionR.txt",header=FALSE)
## s plots
pdf("srevL1.pdf",width=12,height=8)
par(mfrow=c(2,1))
par(mar=c(5.5,5.5,0.5,0.5))
sym<-19
plot(1:1024,L[,1],ylim=c(-0.5,0.5),cex.lab=1.7,cex.axis=1,xlab="",ylab="selection on lentil",pch=sym,cex=0.6,type="n")
segments(1:1024,L[,4],1:1024,L[,5],lwd=0.3,lty=3,col="darkgray")
points(1:1024,L[,1],pch=sym,cex=0.2)
abline(h=0,lty=2,lwd=1.1)
plot(1:1024,R[,1],ylim=c(-0.5,0.5),cex.lab=1.7,cex.axis=1,xlab="locus (SNP)",ylab="selection on mung",pch=sym,cex=0.6,type="n")
segments(1:1024,R[,4],1:1024,R[,5],lwd=0.3,lty=3,col="darkgray")
points(1:1024,R[,1],pch=sym,cex=0.2)
abline(h=0,lty=2,lwd=1.1)
dev.off()
pdf("srevL2.pdf",width=12,height=8)
par(mfrow=c(2,1))
par(mar=c(5.5,5.5,0.5,0.5))
sym<-19
plot(1:1024,L[,6],ylim=c(-0.5,0.5),cex.lab=1.7,cex.axis=1,xlab="",ylab="selection on lentil",pch=sym,cex=0.6,type="n")
segments(1:1024,L[,9],1:1024,L[,10],lwd=0.3,lty=3,col="darkgray")
points(1:1024,L[,6],pch=sym,cex=0.2)
abline(h=0,lty=2,lwd=1.1)
plot(1:1024,R[,6],ylim=c(-0.5,0.5),cex.lab=1.7,cex.axis=1,xlab="locus (SNP)",ylab="selection on mung",pch=sym,cex=0.6,type="n")
segments(1:1024,R[,9],1:1024,R[,10],lwd=0.3,lty=3,col="darkgray")
points(1:1024,R[,6],pch=sym,cex=0.2)
abline(h=0,lty=2,lwd=1.1)
dev.off()
pdf("srevL3.pdf",width=12,height=8)
par(mfrow=c(2,1))
par(mar=c(5.5,5.5,0.5,0.5))
sym<-19
plot(1:1024,L[,11],ylim=c(-0.5,0.5),cex.lab=1.7,cex.axis=1,xlab="",ylab="selection on lentil",pch=sym,cex=0.6,type="n")
segments(1:1024,L[,14],1:1024,L[,15],lwd=0.3,lty=3,col="darkgray")
points(1:1024,L[,11],pch=sym,cex=0.2)
abline(h=0,lty=2,lwd=1.1)
plot(1:1024,R[,11],ylim=c(-0.5,0.5),cex.lab=1.7,cex.axis=1,xlab="locus (SNP)",ylab="selection on mung",pch=sym,cex=0.6,type="n")
segments(1:1024,R[,14],1:1024,R[,15],lwd=0.3,lty=3,col="darkgray")
points(1:1024,R[,11],pch=sym,cex=0.2)
abline(h=0,lty=2,lwd=1.1)
dev.off()
## number of loci with non-zero s
sum(L[,4] > 0 | L[,5] < 0)
sum(L[,9] > 0 | L[,10] < 0)
sum(L[,14] > 0 | L[,15] < 0)
sum(R[,4] > 0 | R[,5] < 0)
sum(R[,9] > 0 | R[,10] < 0)
sum(R[,14] > 0 | R[,15] < 0)
ids<-read.table("locusidsRev.txt",header=FALSE)
length(unique(ids[(L[,4] > 0 | L[,5] < 0),1]))
length(unique(ids[(L[,9] > 0 | L[,10] < 0),1]))
length(unique(ids[(L[,14] > 0 | L[,15] < 0),1]))
length(unique(ids[(R[,4] > 0 | R[,5] < 0),1]))
length(unique(ids[(R[,9] > 0 | R[,10] < 0),1]))
length(unique(ids[(R[,14] > 0 | R[,15] < 0),1]))
## AP vs CN plot
aplist<-vector("list",3)
cnt<-1
pdf("tradeoffs.pdf",width=4,heigh=12)
par(mfrow=c(3,1))
par(mar=c(4.5,5.5,2,1))
par(pty='s')
sym<-21
prefix<-rep(NA,11)
prefix[1]<-"(a) L1 x L1R"
prefix[6]<-"(b) L2 x L2R"
prefix[11]<-"(c) L3 x L3R"
cs<-c("gray",brewer.pal(4,"Dark2")[c(2,1,3,4)])
#cs<-c("gray","orange","blue","red","black")
x<-c(1,6,11)
nmin<--0.01
nmax<-0.01
npnt<-0.00497
cnts<-matrix(NA,nrow=3,ncol=5)
for(i in x){
lb<-i+3
ub<-i+4
q1<-i+1
q3<-i+2
A<-which((L[,lb] > 0 | L[,ub] < 0) & (R[,q1] > nmin & R[,q3] < nmax & abs(R[,i]) < npnt))
B<-which((R[,lb] > 0 | R[,ub] < 0) & (L[,q1] > nmin & L[,q3] < nmax & abs(L[,i]) < npnt))
C<-which((L[,lb] > 0 | L[,ub] < 0) & (R[,lb] > 0 | R[,ub] < 0))
D<-which((R[,q1] > nmin & R[,q3] < nmax) & (L[,q1] > nmin & L[,q3] < nmax))
if(i == 11){plot(L[,i],R[,i],type="n",ylab="reversion",xlab="lentil",cex.axis=1.2,cex.lab=1.9)}
if(i < 11){plot(L[,i],R[,i],type="n",ylab="reversion",xlab="",cex.axis=1.2,cex.lab=1.9)}
z<-unique(c(A,B,C,D))
LCN<-length(A)
RCN<-length(B)
AP<-length(C)
pcs<-rep(NA,1024)
pcs[-z]<-cs[1]
pcs[A]<-cs[2]
pcs[B]<-cs[3]
pcs[C]<-cs[4]
pcs[D]<-cs[5]
points(L[-z,i],R[-z,i],col=pcs[-z],pch=19,cex=0.5)
points(L[z,i],R[z,i],col=pcs[z],pch=sym,cex=1)
abline(h=0,lty=2)
abline(v=0,lty=2)
tit<-paste(prefix[i],", LCN = ",LCN,", RCN = ",RCN,", AP = ",AP,sep="")
title(main=tit,adj=0)
if(i == 1){legend(-0.24,-0.09,c("Unk","LCN","RCN","AP","Neu"),pch=c(19,rep(sym,4)),col=cs)}
aplist[[cnt]]<-C
cnts[cnt,]<-c(length(A),length(B),length(C),length(D),1024-length(z))
cnt<-cnt+1
}
dev.off()
Plots and analyses of tradeoffs in the rapid adaptation model (tradeoffsRapid.R):
lb<-i+3
ub<-i+4
q1<-i+1
q3<-i+2
A<-which((L[,lb] > 0 | L[,ub] < 0) & (R[,q1] > nmin & R[,q3] < nmax & abs(R[,i]) < npnt))
B<-which((R[,lb] > 0 | R[,ub] < 0) & (L[,q1] > nmin & L[,q3] < nmax & abs(L[,i]) < npnt))
C<-which((L[,lb] > 0 | L[,ub] < 0) & (R[,lb] > 0 | R[,ub] < 0))
D<-which((R[,q1] > nmin & R[,q3] < nmax) & (L[,q1] > nmin & L[,q3] < nmax))
if(i == 11){plot(L[,i],R[,i],type="n",ylab="reversion",xlab="lentil",cex.axis=1.2,cex.lab=1.9)}
if(i < 11){plot(L[,i],R[,i],type="n",ylab="reversion",xlab="",cex.axis=1.2,cex.lab=1.9)}
z<-unique(c(A,B,C,D))
LCN<-length(A)
RCN<-length(B)
AP<-length(C)
pcs<-rep(NA,1024)
pcs[-z]<-cs[1]
pcs[A]<-cs[2]
pcs[B]<-cs[3]
pcs[C]<-cs[4]
pcs[D]<-cs[5]
points(L[-z,i],R[-z,i],col=pcs[-z],pch=19,cex=0.5)
points(L[z,i],R[z,i],col=pcs[z],pch=sym,cex=1)
abline(h=0,lty=2)
abline(v=0,lty=2)
tit<-paste(prefix[i],", LCN = ",LCN,", RCN = ",RCN,", AP = ",AP,sep="")
title(main=tit,adj=0)
if(i == 1){legend(-0.24,-0.09,c("Unk","LCN","RCN","AP","Neu"),pch=c(19,rep(sym,4)),col=cs)}
aplist[[cnt]]<-C
cnts[cnt,]<-c(length(A),length(B),length(C),length(D),1024-length(z))
cnt<-cnt+1
}
dev.off()
## number of loci with non-zero s
sum(L[,4] > 0 | L[,5] < 0)
sum(L[,9] > 0 | L[,10] < 0)
sum(L[,14] > 0 | L[,15] < 0)
sum(R[,4] > 0 | R[,5] < 0)
sum(R[,9] > 0 | R[,10] < 0)
sum(R[,14] > 0 | R[,15] < 0)
ids<-read.table("locusidsRev.txt",header=FALSE)
length(unique(ids[(L[,4] > 0 | L[,5] < 0),1]))
length(unique(ids[(L[,9] > 0 | L[,10] < 0),1]))
length(unique(ids[(L[,14] > 0 | L[,15] < 0),1]))
length(unique(ids[(R[,4] > 0 | R[,5] < 0),1]))
length(unique(ids[(R[,9] > 0 | R[,10] < 0),1]))
length(unique(ids[(R[,14] > 0 | R[,15] < 0),1]))
aplist<-vector("list",3)
cnt<-1
pdf("tradeoffsRapid.pdf",width=4,heigh=12)
par(mfrow=c(3,1))
par(mar=c(4.5,5.5,2,1))
par(pty='s')
sym<-21
prefix<-rep(NA,11)
prefix[1]<-"(a) L1 x L1R"
prefix[6]<-"(b) L2 x L2R"
prefix[11]<-"(c) L3 x L3R"
cs<-c("gray",brewer.pal(4,"Dark2")[c(2,1,3,4)])
#cs<-c("gray","orange","blue","red","black")
x<-c(1,6,11)
nmin<--0.01
nmax<-0.01
npnt<-0.00497
cnts<-matrix(NA,nrow=3,ncol=5)
for(i in x){
lb<-i+3
ub<-i+4
q1<-i+1
q3<-i+2
A<-which((L[,lb] > 0 | L[,ub] < 0) & (R[,q1] > nmin & R[,q3] < nmax & abs(R[,i]) < npnt))
B<-which((R[,lb] > 0 | R[,ub] < 0) & (L[,q1] > nmin & L[,q3] < nmax & abs(L[,i]) < npnt))
C<-which((L[,lb] > 0 | L[,ub] < 0) & (R[,lb] > 0 | R[,ub] < 0))
D<-which((R[,q1] > nmin & R[,q3] < nmax) & (L[,q1] > nmin & L[,q3] < nmax))
if(i == 11){plot(L[,i],R[,i],type="n",ylab="reversion",xlab="lentil",cex.axis=1.2,cex.lab=1.6)}
if(i < 11){plot(L[,i],R[,i],type="n",ylab="reversion",xlab="",cex.axis=1.2,cex.lab=1.6)}
z<-unique(c(A,B,C,D))
LCN<-length(A)
RCN<-length(B)
AP<-length(C)
pcs<-rep(NA,1024)
pcs[-z]<-cs[1]
pcs[A]<-cs[2]
pcs[B]<-cs[3]
pcs[C]<-cs[4]
pcs[D]<-cs[5]
points(L[-z,i],R[-z,i],col=pcs[-z],pch=19,cex=0.5)
points(L[z,i],R[z,i],col=pcs[z],pch=sym,cex=1)
abline(h=0,lty=2)
abline(v=0,lty=2)
tit<-paste(prefix[i],", LCN = ",LCN,", RCN = ",RCN,", AP = ",AP,sep="")
title(main=tit,adj=0)
if(i == 1){legend(-0.62,0.22,c("Unk","LCN","RCN","AP","Neu"),pch=c(19,rep(sym,4)),col=cs)}
aplist[[cnt]]<-C
cnts[cnt,]<-c(length(A),length(B),length(C),length(D),1024-length(z))
cnt<-cnt+1
}
dev.off()
Additional numerical summaries of these results were calculated on the fly in R and are summarized in the manuscript (pasted below too):
parallelism = posterior probs
constant: 79 (11.7\%) with pp > 95\%; 407 (60.5\%) with pp < 50\%
% stronger selection on parallel snps, 2.15x; z = 79 > 95\%
%> mean(as.numeric(as.matrix(abs(L[z,c(1,6,11)]))))
%[1] 0.1320301
%> mean(as.numeric(as.matrix(abs(L[-z,c(1,6,11)]))))
%[1] 0.06141516
rapid: 108 (16.0\%) with pp > 95\%; 322 (47.8\%) with pp < 50\%
% stronger selection on parallel snps, 3.04x; z = 108 > 95\%
%> mean(as.numeric(as.matrix(abs(Lr[z,c(1,6,11)]))))
%[1] 0.4029769
%> mean(as.numeric(as.matrix(abs(Lr[-z,c(1,6,11)]))))
%[1] 0.1326632
cors across lines
constant
> cor(L[,1],L[,6])
[1] 0.3698586
> cor(L[,1],L[,11])
[1] 0.5811047
> cor(L[,6],L[,11])
[1] 0.6796028
rapid
> cor(Lr[,1],Lr[,6])
[1] 0.7985814
> cor(Lr[,1],Lr[,11])
[1] 0.8357184
> cor(Lr[,6],Lr[,11])
[1] 0.9468155
lentil adapt correlation between s on L in constant vs. rapid
> cor(L[,1],Lx[,1])
[1] 0.908319
> cor(L[,6],Lx[,6])
[1] 0.8624496
> cor(L[,11],Lx[,11])
[1] 0.9247745
average of ratios vs. ratio of averages below
> mean(abs(L[,1])/abs(Lx[,1]))
[1] 29.1245
> mean(abs(L[,6])/abs(Lx[,6]))
[1] 12.87685
> mean(abs(L[,11])/abs(Lx[,11]))
[1] 15.75747
estimates of S, numbers in table, mean and width of CIs constant
vs. rapid
constant mean and sd of S by line
> apply(abs(L[,c(1,6,11)]),2,mean)
V1 V6 V11
0.08884180 0.06977323 0.05049781
> apply(abs(L[,c(1,6,11)]),2,sd)
V1 V6 V11
0.05945690 0.06435937 0.05487684
rapid, about 2.6x as high for mean
> apply(abs(Lr[,c(1,6,11)]),2,mean)
V1 V6 V11
0.1980953 0.1677216 0.1623088
> apply(abs(Lr[,c(1,6,11)]),2,sd)
V1 V6 V11
0.1515386 0.1568458 0.1524746
comparison of width of 95% ETIPs
L1 2.58 x wider rapid
> mean(L[,5]-L[,4])
[1] 0.1910882
> mean(Lr[,5]-Lr[,4])
[1] 0.492943
L2 3.25 x wider rapid
> mean(L[,10]-L[,9])
[1] 0.1535428
> mean(Lr[,10]-Lr[,9])
[1] 0.4987423
L3 3.60 x wider rapid
> mean(L[,15]-L[,14])
[1] 0.1438567
> mean(Lr[,15]-Lr[,14])
[1] 0.5171537
REVERSION results, figures and tables for key numbers
reversion correlation between s on L in constant vs. rapid
> cor(L[,1],Lx[,1])
[1] 0.8877324
> cor(L[,6],Lx[,6])
[1] 0.874945
> cor(L[,11],Lx[,11])
[1] 0.9003058
average of ratios
> mean(abs(L[,1])/abs(Lx[,1]))
[1] 7.758508
> mean(abs(L[,6])/abs(Lx[,6]))
[1] 6.877541
> mean(abs(L[,11])/abs(Lx[,11]))
[1] 5.995486
average selection on L vs M
constant L/M = 3.08; rapid L/M = 10.56
mean(as.numeric(as.matrix(abs(L[z,c(1,6,11)]))))
[1] 0.1272749
> mean(as.numeric(as.matrix(abs(Lr[z,c(1,6,11)]))))
[1] 0.3966057
> mean(as.numeric(as.matrix(abs(R[z,c(1,6,11)]))))
[1] 0.04135309
> mean(as.numeric(as.matrix(abs(Rr[z,c(1,6,11)]))))
[1] 0.03753991
correlations between selection on L vs M, all 1024 snps
constant
L1
data: L[, 1] and R[, 1]
t = -3.0673, df = 1022, p-value = 0.002217
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.15585910 -0.03444865
sample estimates:
cor
-0.09550906
L2
data: L[, 6] and R[, 6]
t = 1.5867, df = 1022, p-value = 0.1129
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.01172423 0.11049975
sample estimates:
cor
0.04957335
L3
data: L[, 11] and R[, 11]
t = 0.7692, df = 1022, p-value = 0.4419
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.03726210 0.08519123
sample estimates:
cor
0.0240548
rapid
L1
data: Lr[, 1] and Rr[, 1]
t = 5.2807, df = 1022, p-value = 1.572e-07
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1027377 0.2220193
sample estimates:
cor
0.1629739
L2
data: Lr[, 6] and Rr[, 6]
t = 2.4301, df = 1022, p-value = 0.01526
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.0146033 0.1364260
sample estimates:
cor
0.07579747
L3
data: Lr[, 11] and Rr[, 11]
t = -0.999, df = 1022, p-value = 0.318
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.09231891 0.03008597
sample estimates:
cor
-0.03123358